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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,174 Documents
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Control Strategy of Cascade STATCOM based on Internal Model Theory Zhenglong Xia; Liping Shi; Li Qianqian
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: March 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Internal model control (IMC) method enables the system to be of good dynamic and steady performance, which is simple, and is easy to be implemented. In allusion to the cascade STATCOM feature of high order, instability, multi-variable, non-linearity and tight coupling, the mathematical model of cascade STATCOM in d-q-0 coordinates was deduced. Decoupling model of cascade STATCOM was given by Internal Model Control principle, computer simulation and experiment results were also given. Results show that with IMC, 3-phase currents control method of cascade STATCOM has good tracking performance and control precision both in a-b-c coordinates and in d-q-0 coordinates, and also achieves excellent current compensation results. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4452
Apply AcryditeTM Gel Separation to Solve Time-Table Problem Zhixiang Yin; Min Chen
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 5: September 2012
Publisher : Institute of Advanced Engineering and Science

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Abstract

Time-table problem is a classical NP-complete problem. Algorithm of DNA computing for time-table problem was obtained with introducing the technology of AcryditeTM gel separation. Each class period viewed as a graph vertex was mapped into DNA molecules chain. With the probe coding, the gel column was constructed to arrange the order of DNA chain through biological reaction. The problem was solved by gel column that performs the basic core processing and extraction that makes the result visible. The minimum number of cycles of arrangement was the minimum number of class hours. The simulation results show that the algorithm compared with others is very easy and feasible. DOI: http://dx.doi.org/10.11591/telkomnika.v10i5.1271
An Efficient Environmental Channel Modelling in 802.11p MAC Protocol for V2I Neelambike S; Chandrika J
Indonesian Journal of Electrical Engineering and Computer Science Vol 7, No 2: August 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v7.i2.pp404-414

Abstract

Recent development in communication of wireless communication for automobile industry have aided the growth of SITS (Smart Intelligent Transport System) which solves numerous vehicular based communication service concerns like traffic congestion, accidental mishap etc. VANET (Vehicular Ad-hoc Network) a characteristic class of MANET (Mobile ad-hoc Network) which is a fundamental element of SITS in which the moving vehicles inter connected and communicates with each other remotely. Wireless technologies play an important part in assisting both Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) correspondence in VANET. The existing scheduling technique does not consider the environmental factor which affects the throughput performance and increases packet drop rate which result in degradation of service quality. Here in this work the author propose a RHU (Rural, Highway and Urban) environment model considering the environmental factor. The efficient environmental model algorithm is incorporated into slotted aloha in IEEE 802.11p MAC protocols which aided as a spine for assisting both safety application and non-Safety applications. Experiments are conducted for collision and throughput efficiency for varied traffic load and speed of vehicle. The experimental result shows the proposed environmental model impact on collision and throughput efficiency for varied environment and thus helps improving QoS for VANET application.
A Review on Various Sniffing Attacks and its Mitigation Techniques B. Prabadevi; N. Jeyanthi
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i3.pp1117-1125

Abstract

Security in the era of digital computing plays a vital role. Of various attacks in the field of computing, Distributed Denial of service (DDoS) attacks, Man-in-the-Middle Attack (MITM) and data theft have their major impact on the emerging applications. The sniffing attacks, one of the most prominent reasons for DDoS attacks, are the major security threats in the client-server computing. The content or packet sniffer snorts the most sensitive information from the network and alters or disturbs the legitimate functionality of the victim system. Therefore it is extremely important to have a greater knowledge on these vulnerabilities, their issues, and various mitigation techniques. This study analyses the existing sniffing attacks, variations of sniffing attacks and prevention or detection mechanisms. The reasons for most vital Ransomware are also discussed.
Automating quranic verses labeling using machine learning approach A. Adeleke; N. Samsudin; A. Mustapha; S. Ahmad Khalid
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp925-931

Abstract

Classification of Quranic verses into predefined categories is an essential task in Quranic studies. However, in recent times, with the advancement in information technology and machine learning, several classification algorithms have been developed for the purpose of text classification tasks. Automated text classification (ATC) is a well-known technique in machine learning. It is the task of developing models that could be trained to automatically assign to each text instances a known label from a predefined state. In this paper, four conventional ML classifiers: support vector machine (SVM), naïve bayes (NB), decision trees (J48), nearest neighbor (k-NN), are used in classifying selected Quranic verses into three predefined class labels: faith (iman), worship (ibadah), etiquettes (akhlak). The Quranic data comprises of verses in chapter two (al-Baqara) of the holy scripture. In the results, the classifiers achieved above 80% accuracy score with naïve bayes (NB) algorithm recording the overall highest scores of 93.9% accuracy and 0.964 AUC.
A Dual-Microphone Speech Enhancement Algorithm for Close-Talk System Yi Jiang; Zhenming Feng; Yuanyuan Zu; Xi Lu
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 6: June 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i6.pp4475-4484

Abstract

While human listening is robust in complex auditory scenes, current speech enhancement algorithms do not perform well in noisy environments, even close-talk system is used. This paper addresses the robustness in dual microphone embedded close talk system by employing a computational auditory scene analysis (CASA) framework. The energy difference between the two microphones is used as the primary separation cue to estimate the ideal binary mask (IBM). We also use voice activity detection to find the noise periods, and update the separation critical value. Generalization interference locations and reverberant conditions are used to examine performance of the proposed system. Evaluation and comparison show that the proposed system outperforms other two systems on the test conditions. DOI : http://dx.doi.org/10.11591/telkomnika.v12i6.5485
Modified Allan Variance Analysis on Random Errors of MINS Bin Fang; Xiaoqi Guo
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 3: March 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

Allan variance method is a useful tool for analyzing the random errors, but the confidence on the estimate would be lower when the data length became shorter, therefore the modified Allan variance is deduced to analysis the random errors of MEMS inertial sensors (MINS). The definition and limitation of Allan variance are presented first, and then the modified Allan variance is deduced. Allan variance method is compared with modified Allan variance by identifying the simulated 1/f noises, meanwhile the results are illuminated. In the end, the random errors of MEMS inertial sensors were analyzed by the proposed methods. The characteristics of MEMS accelerometers’ and MEMS gyros’ stochastic errors are identified and quantified. The derived error model can be applied further to our attitude and heading reference system of the underwater robot. DOI: http://dx.doi.org/10.11591/telkomnika.v11i3.2190
Content Based Image Retrieval Using Lacunarity and Color Moments Of Skin Diseases I Gusti Ayu Triwayuni; I Ketut Gede Darma Putra; I Putu Agus Eka Pratama
Indonesian Journal of Electrical Engineering and Computer Science Vol 9, No 1: January 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v9.i1.pp243-248

Abstract

The research conducted contributed in the form of CBIR application which was developed using texture and color feature extraction in searching the contents information of an object of skin disease image. The textured feature is extracted using Lacunarity, while for color feature extraction using Color Moments as well as a combination of both methods. The results of color characteristic extraction test using Color Moments Method yielded images corresponding to 100% similarity percentages and experimentation of texture characteristic extraction using Lacunarity Method yielded images corresponding to a percentage of suitability of 25%, followed by a combined test of both methods and the normalization process produces images corresponding to a percentage of conformity of 60%.
Hybrid enhanced ICA & KSVM based brain tumor image segmentation Thrivikram Bathini; Baswaraj Gadgay
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp478-489

Abstract

Medical image processing is an important aspect in diagnosis and treatment strategy. The tremendous volume of medical data has accelerated the need for automated analysis of this image, more so in the case Magnetic Resonance Imaging (MRI). An improved K-means algorithm and EM algorithm have been combined in the proposed approach to produce a hybrid strategy for better clustering and segmentation using Enhanced ICA. A classifier for based on Support Vector Machine (SVM) has been formulated and employed for the classification of brain tumors in Magnetic Resonance Images (MRI). The proposed SVM classifier used a kernel in the form of Gaussian radial basis function kernel (GRB kernel) to improve the classifier performance. The performance of the classifier has been validated through expert clinical opinion and calculation of performance measures. The results amply illustrate the suitability of the proposed classifier.
Prediction of energy consumption using recurrent neural networks (RNN) and nonlinear autoregressive neural network with external input (NARX) Wan Muhammad Zafri Wan Yahaya; Fadhlan Hafizhelmi Kamaru Zaman; Mohd Fuad Abdul Latip
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i3.pp1215-1223

Abstract

Recurrent Neural Networks (RNN) and Nonlinear Autoregressive Neural Network with External Input (NARX) are recently applied in predicting energy consumption. Energy consumption prediction for depth analysis of how electrical energy consumption is managed on Tower 2 Engineering Building is critical in order to reduce the energy usage and the operational cost. Prediction of energy consumption in this building will bring great benefits to the Faculty of Electrical Engineering UiTM Shah Alam. In this work, we present the comparative study on the performance of prediction of energy consumption in Tower 2 Engineering Building using RNN and NARX method. The model of RNN and NARX are trained using data collected using smart meters installed inside the building. The results after training and testing using RNN and NARX show that by using the recorded data we can accurately predict the energy consumption in the building. We also show that RNN model trained with normalized data performs better than NARX model.

Page 38 of 918 | Total Record : 9174

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